Calibrating Conservatism for Scalable Oversight
2026-05-27 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors developed a method called Calibrated Collective Oversight (CCO) to help humans keep control over smart AI systems that make their own plans and decisions. CCO works by combining different signals about the AI's actions and applying penalties when the actions worry human overseers, making sure risky actions are avoided but good ones are still allowed. Their approach adjusts these penalties on the fly to meet safety goals without assuming anything specific about the environment. Tests showed that CCO helps weaker human overseers effectively control stronger AI agents and reduces unethical behavior while keeping rewards high.
Agentic AIScalable oversightConservative baselineAttainable Utility PreservationConformal Decision TheoryEthical AISequential decision makingStatistical guaranteesSafety constraintsReward preservation
Authors
William Overman, Mohsen Bayati
Abstract
Agentic AI systems capable of autonomous planning and extended environmental interaction pose a fundamental control problem: how can humans maintain meaningful oversight of systems that may exceed their own capabilities? Existing approaches to scalable oversight rely on complex assumptions, remain largely heuristic, or lack practical methods for sequential settings with statistical guarantees. We introduce Calibrated Collective Oversight (CCO), which aggregates diverse auxiliary scoring functions into a penalty measuring deviation from a conservative baseline. Inspired by Attainable Utility Preservation, CCO enables collective conservatism: actions face a penalty proportional to overseer concern, so high-utility actions are still selected when overseers find them unobjectionable and overridden only when concern accumulates. CCO calibrates this conservatism online using Conformal Decision Theory, ensuring that undesirable outcomes remain below a user-specified target threshold with finite-time bounds and no distributional assumptions. On a modified version of SWE-bench, weaker overseers successfully constrain an adversarially misaligned stronger agent; on MACHIAVELLI, CCO substantially reduces ethical violations while preserving reward. In both settings, empirical violation rates closely match the specified targets, as predicted by the theory.